1. Field of the Invention
This invention relates generally to the field of intrusion detectors and to the field of computer monitoring systems. More specifically, this invention employs neural network connection-based computing to monitor the location and identity of people, animals, and objects within an indoor or outdoor area for the purpose of intrusion detection, theft deterrence, and accident prevention.
2. The Background Art
In the prior art, there are various references related to the field of the invention. The reader is directed to United States patents in class 340, particularly subclasses 500-599 and subclass 825 , class 364, class 395 and class 358 for illustrative examples of the state of the prior art.
Heretofore, intrusion detection systems and property monitoring systems have only been able to provide binary on/off alert information to users. Even sophisticated systems which employ multiple sensors can only resolve an alert to a particular sector of the area under surveillance. Existing systems are not able to determine, and convey to system users, the precise location, identity, and movement of one or more intruders. This invention provides this capability and more, by combining analog and broadband sensors with the intelligence of a class of computer called a neural network, to bring about a fundamentally new type of intelligent area monitoring system.
The neural network computer uses a different principle to process information than does a traditional rule-based computer. The neural network is a connection-based computer, meaning that it processes information based on the connections between many small processors; as opposed to traditional computer processors which apply a sequence of rules to input information. The neural network processor works somewhat like a biological neural system in that it compares the inputs on each neuron to many other neurons, and formulates its output based on the aggregate of the inputs.
Neural network processors appear destined to dramatically expand the range of uses for computers. Many of the artificial intelligence applications that the computer industry has been striving in vain to achieve using rule-based computers, seem to come naturally to connection-based computers. Neural network processors not only facilitate a new generation of intrusion detection and computer monitoring systems, they allow commercial implementations of such systems to provide new functionality. Examples of new functionality include authentication of residents or employees by pattern matching their speech and optical images; speech recognition such that the system would alert another resident if someone were to call for help or cry; and behavior modeling so that the system would be able to issue an alert if someone were to fall in the shower, or a child were to walk toward the street.
The present invention employs multiple sensors of various types which may be arrayed around the area to be monitored. In the stationary array implementation of the invention, the sensors are arrayed around the area to be monitored in a fixed fashion. In the mobile array implementation of the invention, the sensors are pre-positioned in a fixed spacial relationship to one another, and the mobile array may be moved from location to location. For example, if the user desires to install a security system in a building, then the stationary array would be employed due the permanence of the system. If, however, a movie crew wished to protect its equipment at night which filming on location, the mobile array could be used to provide security to the specific area where the equipment is stored.
Sensors are commercially available to detect sound, vibration, and emissions of various segments of the electromagnetic spectrum such as infrared, visual, and microwave. Prior art references of security assessment systems, intrusion detection systems, and computer monitoring systems, employ sensors with discrete outputs, usually binary, and thereby convey only on/off alert information to system users. U.S. Pat. No. 4,857,912 in the name of Everett, Jr. et al. and dated Aug. 15, 1989, which is hereby incorporated by reference (the "Everett Patent"), discusses sensors that have "an on and off state". The present invention can provide useful information based on input from such binary sensors; however, a fundamentally new type of intelligent area monitoring is achieved when using the analog output sensors described above. Analysis of the analog data by the neural network computer allows this invention to not only detect the presence of people, animals, or objects; but to deduce their precise location at any instance. Additionally, the neural network is able to infer the identity of people, animals, or objects, and convey this to users of the monitoring system.
Sensors can be classified in terms of their energy source and their output signals. The simplest of these are passive sensors which detect energy either emitted or reflected by objects within their field of view. Active sensors emit their own energy which is bounced back to the sensor from the objects within their field of view. The present invention is quite effective with passive sensors, active sensors, or a combination of the two types.
As described above, the invention employs an array of various individual sensors used to monitor an area for any changes that occur within the space by sensing various energy forms, such as optical, heat, vibration, sound, etc. Alternatively, it is possible to employ a "field sensor" which is defined herein to consist of an area, such as a screen, array, grid, or other physical structure, having numerous sensing points or sensing elements on it. Those sensing points may be individual discrete sensors or they may be points on a continuous sensing mechanism or sensing screen which is sensitive at infinite intervals across its surface. The field sensor could also consist of a grid or N .times. M array of individual sensors. The field sensor would include sensing elements of the type capable of sensing information considered important to the system, and the field sensor would provide its output to the neural network computer for interpretation, the aggregate of the output being used by the neural network computer to make evaluations and decisions based on its past experience and training.
One of the shortcomings of existing intrusion detection systems is their propensity to give false alarms. Existing systems use rule-based algorithms to determine if an alert should be issued. Common commercial burglar alarms for home and business issue an alert if any sensor on the property exceeds a threshold value. More sophisticated systems, such as that described by the Everett Patent, employ a two stage processing scheme whereby the "on" signals from multiple sensors are added together, and an overall alert is issued when the sum exceeds a threshold value. Even elaborate rule-based systems are typically unable to reduce false alarms to an acceptable level because the variability encountered even in nominal environments exceeds the logic of these systems.
The present invention does not suffer from these shortcomings because it is based on an entirely different principle than rule-based systems. Connection-based systems not only accommodate a much wider range of variability, but improve their accuracy and discrimination with regular use. More importantly however, this invention does not merely issue alerts, but displays the exact location of one or more intruders in a graphic image of the monitored area. The invention can be used to issue alerts in response to changes within a space that exceed previously observed behavioral norms. This allows the user to understand exactly what has caused the alert, and thereby qualify whether the situation warrants a particular level of alert. The user can then correct the system if necessary so that subsequent similar situations will be correctly interpreted by the system.